Ultrasonic intelligent testing technology for sandwich honeycomb composite structure of aircraft horizontal tail

doi:10.19936/j.cnki.2096-8000.20251028.013

Abstract

Abstract: The honeycomb composite structure of aircraft horizontal tail has large size, complex material structure and high quality requirements. The water-jet ultrasonic focusing imaging detection technology can realize the imaging detection of honeycomb structure. The evaluation of a large number of detected images depends on the rich engineering experience and high-intensity work of technicians, which inevitably leads to poor evaluation reliability due to the influence of subjective factors. Therefore, an intelligent recognition technology of ultrasonic C-scan detection image of honeycomb composite material of aircraft horizontal tail based on deep learning network is proposed. Firstly, the C-scan detection image of the horizontal tail of the aircraft is collected by the water-jet ultrasonic focusing detection method, and the data set of the ultrasonic detection image of the horizontal tail of the aircraft is constructed and expanded. Secondly, based on the amplitude distribution of the detection signal corresponding to the detection image, the detection image is divided into three target area categories according to the degree of bonding integrity. Thirdly, the Faster R-CNN network is constructed and optimized to form an intelligent recognition network for small feature changes in the ultrasonic C-scan area of honeycomb composite structures. Finally, the performance of the intelligent recognition model was measured by experimental methods to verify its ability to evaluate the ultrasonic C-scan images of honeycomb structures. The research results show that the average accuracy of the intelligent model based on deep learning for the classification and recognition of honeycomb composite materials reaches 88.2%, and the average accuracy of the worst bonding area (three class of region) can reach 91.9%, which can be used for classification and statistics of ultrasonic C-scan detection images of honeycomb composite structures.

Key words: honeycomb composites, Faster R-CNN, water-jet ultrasonic focus testing, deep learning, intelligent recognition

CLC Number:

TB332

TU Simin, CHEN Zhenhua, ZHANG Junyan, TU Dongkun, XU Yunlin, LU Chao. Ultrasonic intelligent testing technology for sandwich honeycomb composite structure of aircraft horizontal tail[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(10): 83-90.

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